SimHash Content Fingerprinting & Near-Duplicate Detection is a paid API for AI agents from x402-deployer.x402-deployer.workers.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Computes a 64-bit SimHash fingerprint over token-level k-shingles (default k=3) for near-duplicate content detection, returning hex and decimal forms plus token and shingle counts.
SimHash / 64-bit content fingerprint / near-duplicate detection / dedup hashing / locality-sensitive hash. Pure-local 64-bit SimHash over token-level k-shingles (default k=3) using FNV-1a. Two SimHashes are 'close' (small Hamming distance) iff the underlying texts share many shingles. Returns hex + decimal forms plus token + shingle counts. Useful for content dedup pipelines, plagiarism detection, and bot-content clustering.
Returns a 64-bit SimHash of the input text in both hex and decimal forms, along with the number of tokens and shingles processed. Two results with a small Hamming distance indicate the underlying texts share many shingles and are likely near-duplicates.
POSThttps://x402-deployer.x402-deployer.workers.dev/content-simhashUse this endpoint when you need a fast, purely local (no external model calls) 64-bit fuzzy fingerprint for text similarity or deduplication. Prefer it over exact hash functions (MD5/SHA) when you want near-duplicate detection that tolerates minor edits, and over embedding-based similarity when you want a lightweight, deterministic, reproducible signal without LLM costs.
{
"k": 3,
"text": "Artificial intelligence is transforming the way we work, live, and communicate with each other every day."
}| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
{
"source": "DIY SimHash (FNV-1a 64)",
"hash_hex": "0x5b37c9ff1edccc9d",
"hash_int": "6572944278742158493",
"bit_count": 64,
"text_chars": 105,
"token_count": 16,
"shingle_size": 3,
"shingle_count": 14
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